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My Summer at Willow Garage

Benjamin CohenUniversity of Pennsylvania

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The Bosses

Dr. Sachin Chitta

my mentor

Professor Maxim Likhachev

my advisor

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My Summer Goals

SBPL Arm Planner

continue development of new algorithm

integrate into move_arm

perform tabletop manipulation

publish

SBPL Door Planner(Sachin did all of the integration. I'm working on the algorithm and

debugging it with him)continue development of algorithm

make a sweet video

publish

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SBPL Arm Planner

Goal:

Develop a motion planner for robotic manipulation thatcan robustly plan paths in cluttered environments.

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Why not run IK and plan to a knownvalid joint configuration?

IK is very fast but may not find the 'best' solution

may find a visually awkward solution

may orient the arm very close to obstacles

may choose a solution very close to joint limits

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Why not run IK and plan to a knownvalid joint configuration? (cont'd)

Let the planner optimize the goal joint configurationaccording to the desired cost function:

length of path

smoothnessdistance from obstacles

keep away from joint limits

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Graph Construction

similar to lattice based planners in planningdynamically feasible trajectories

a discretization of the configuration space into aset of states, the edges that connect thestarts are dynamically feasible

edges are pre-defined motion primitives

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Costs

Smoothing Cost

minimize change in velocities between states

keep the same joint(s) moving if possible

Action Cost

minimize the number of actions to the goal

Distance Cost

apply a cost for planning close to obstacles

c s , s ' =ccells 'w

actionc

actions , s ' w

smoothc

smooths , s '

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Heuristic

3D Dijkstra search

robust to obstacles

pre-computed

directs the end effector to the goal pose

difference in orientation of the end effector

Goal

Dijkstra Shortest Path

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ARA : Anytime Random A*

is consistent

guarantees completeness

'anytime results' can trade-off path cost vs.computation time

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ARA : Anytime Random A*

ARA* finds a feasible solution quickly and thencontinually works on improving it until theallocated time runs out.

ARA* uses an admissible heuristic (a bound onthe plan-execution cost) to focus its search

Each solution comes with a bound, , on its sub-optimality:

cost(solution) cost(optimal solution)

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Features

given multiple goals, find the path to goal posewith lowest cost

plan to 'goal region'

plan with path constraints, such as:

keep a cup upright

don't obstruct field of view of forearm cameras (futurework)

plan to a goal pose in collision, capable ofmanipulation (future work)

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Drawbacks

high dependency on the heuristic function

local minima

doesn't describe kinematics of the arm

high dimensionality slows down the search

discretized paths

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Future Work

learn a set of motion primitives with goodcoverage of the workspace and natural lookingmovement

add randomization to escape local minimaR* is an A* type search with randomization

research smoothing methods

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SBPL Door Planner

Goal:

open doors

inward and outward opening doors

right handed and left handed doors

with collision avoidance

guaranteed feasibility

Method:

ARA* in a 4-Dimensional state space{x,y,theta,door interval}

door interval: {0,1} (open or closed)

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Other Packages

Robot Voxelizer (sandbox/robot_voxelizer)

wrapper around Ioan's planning models

voxelize robot bodies, meshes, shapes

PM Wrapper (planning_research/pm_wrapper)

wrapper around Ioan's planning_monitor class

easy collision checking, collision map manipulation

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Other Packages (cont'd)

Arm Command Tool (sandbox/move_arm_tools)

'safe' movement with move_arm

'unsafe' movement for tabletop manipulation

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Thank You!

Scott & Steve

Sachin

MaxIoan and Mrinal

Everyone else!

thanks!